The sarcopenia risk prediction model showed strong discrimination with AUC 0.923 in elderly HFpEF patients in development cohort and AUC 0.937 upon external validation.
Observational (n=356)
Yes
Does a 5-variable risk prediction model accurately identify sarcopenia risk in elderly patients with HFpEF?
A novel 5-variable risk prediction model accurately identifies sarcopenia risk in elderly patients with HFpEF, providing a practical tool for early screening.
Effect estimate: AUC 0.923 in development cohort, AUC 0.937 in external validation cohort (95% CI Development cohort 0.892–0.954, External validation cohort 0.890–0.984)
Chronic heart failure combined with sarcopenia is significantly associated with negative health outcomes in elderly patients. Therefore, early identification of sarcopenia risk in elderly patients with heart failure is crucial for their prognosis, however, there is currently no simple and practical predictive model available for clinicians.This study aimed to develop and externally validate a risk prediction model for sarcopenia in elderly patients with heart failure with preserved ejection fraction. A retrospective study design was employed.A cohort of HFpEF patients from the Geriatrics Department of Ruijin Hospital was used as the development cohort (n = 272) for model construction and internal validation.Variables with significant differences in intergroup comparisons were initially screened, followed by variable compression using LASSO regression.A multivariable logistic regression analysis was ultimately performed to establish the prediction model.The discriminative ability and calibration of the model were assessed using ROC curve and calibration curve, respectively.Subsequently, an independent external validation cohort (n = 84) from Nursing Home in Changning District was used to validate the model’s generalizability and clinical utility through ROC curve analysis, calibration curve analysis, and decision curve analysis. The final model included five predictors: 1, 25OH-VitD3, BMI, NRS2002 score, handgrip strength, and homocysteine. In the development cohort, the model showed strong discriminative ability, with an AUC of 0.923 (95% CI: 0.892–0.954), and was well-calibrated. External validation confirmed its robust performance, yielding an AUC of 0.937 (95% CI: 0.890–0.984). The calibration curve indicated high agreement between predictions and observations, and decision curve analysis demonstrated a favorable net clinical benefit. This study developed and validated the first risk prediction model for sarcopenia tailored to elderly HFpEF patients. The model performed excellently in both internal and external validation, enabling effective identification of high-risk individuals. It offers a practical quantitative tool for early screening and targeted intervention.
Yang et al. (Mon,) conducted a observational in Elderly patients (≥60 years) with heart failure with preserved ejection fraction (HFpEF) classified as NYHA class II-III (n=356). Sarcopenia risk prediction model using 25OH-VitD3, BMI, NRS2002 score, handgrip strength, homocysteine vs. No prediction model (standard assessment) was evaluated on Discrimination ability of sarcopenia risk prediction model for sarcopenia diagnosis based on 2019 AWGS criteria (AUC 0.923 in development cohort, AUC 0.937 in external validation cohort, 95% CI Development cohort 0.892–0.954, External validation cohort 0.890–0.984). The sarcopenia risk prediction model showed strong discrimination with AUC 0.923 in elderly HFpEF patients in development cohort and AUC 0.937 upon external validation.